---
title: "A4F - Shelf Life"
output: 
  flexdashboard::flex_dashboard:
    navbar:
      - { title: "Research", href: "http://roneyfraga.com/dash/2020_A4F", align: right }
      - { title: "People", href: "http://roneyfraga.com/dash/2020_A4F/#pessoas", align: right }
      - { title: "Patent", href: "http://roneyfraga.com/dash/2020_A4F/#pessoas", align: right }
      - { title: "About", href: "http://roneyfraga.com/", align: right }
    social: [ "menu" ]
    source_code: "embed"
    theme: bootstrap #yeti #lumen
    logo: img/logo.png
---

```{r setup, include=FALSE}
options(scipen=999)
library(rmarkdown)
library(flexdashboard)
library(pipeR)
library(tidyverse)
library(rio)
library(ggraph)
library(tidygraph)
library(DT)
library(plotly)
library(visNetwork)
library(igraph)
library(ggthemes)
library(highcharter)
library(lubridate)
library(sparkline)
library(htmlwidgets)
library(printr)
```

# General 


Column {data-width=500 .tabset}
-------------------------------------


### Target


 

  Qual área de pesquisa é emergente?  
 

 

  Qual pesquisador contratar?  
 

 

  Qual patente comprar?  
 

 



### Growth

```{r}
# graphics
import('data/shelf_life_growth.txt') %>>% 
    as_tibble %>>% 
    rename(PY = V1, publications = V2 ) %>>% 
    dplyr::filter(PY %in% c(1980:2019)) %>>% 
    dplyr::arrange(PY) %>>% 
    dplyr::mutate(trend=1:n()) %>>% 
    (. -> d)

# export(d, '~/OneDrive/Rworkspace/SASUniversityEdition/myfolder/shelf_life/shelf_life.csv')

d$lnp <- log(d$publications)

# ajustar parametros via mqo 
m1 <- lm(lnp ~ trend, data=d)

# summary(m1)

beta0 <- m1$coefficients[[1]]
beta1 <- m1$coefficients[[2]]

# modelo não linear
# 1980 é o primeiro ano da série
m2 <- nls(publications ~ b0*exp(b1*(PY-1980)), start = list(b0=beta0, b1=beta1), data=d)

# publications estimado
d$predicted <- 12.159638*exp(0.121922*(d$PY-1980))

d %>>% 
    mutate(Publications=publications, Year=PY) %>>% 
    mutate(predicted=round(predicted,0)) %>>% 
    (. -> d2)

hchart(d2, "column", hcaes(x = Year, y = Publications), name = "Publications", showInLegend = TRUE) %>>%
    hc_add_series(d2, "line", hcaes(x = Year, y = predicted), name = "Predicted", showInLegend = TRUE) %>>% 
    hc_add_theme(hc_theme_elementary()) %>>%
    hc_navigator( enabled = TRUE) 
```


### Groups Growth
    
```{r}

netcoup <- import('data/netcoup.rds')
a <- import('data/netcoup_grupos.rds')

netcoup %>>% 
    activate(nodes) %>>% 
    as_tibble %>>% 
    dplyr::filter(!is.na(grupo)) %>>% 
    group_by(PY,grupo) %>>% 
    tally(sort=TRUE) %>>% 
    arrange(grupo,desc(PY)) %>>% 
    ungroup %>>% 
    dplyr::filter(PY %in% c(2000:2019)) %>>% 
    dplyr::mutate(Group=grupo,Publications = n, Year = PY) %>>% 
    (. -> grupoAno)

hchart(grupoAno, "line", hcaes(x = Year, y = Publications, group = Group), fillOpacity = 0.2) %>>% 
    hc_add_theme(hc_theme_elementary())  %>>% 
    hc_navigator( enabled = TRUE) 
```


### Groups Description
    
```{r}
data.frame(Group=paste0('g',1:13),Description='algum texto para descrever o grupo') %>>% 
    datatable(options=list(pageLength=13, dom = 'tip'), rownames=F)  
```


Column {data-width=500 .tabset}
-------------------------------------

### Shelf Life


 


  Adicionar uma sentença sobre Shelf Life, para catecterizar a área de pesquisa.  
 


 


> - __Shelf Life__
> - 13,516 Registers \n
> - 12.9% Growth Rate  \n
> - 5.6 Years Doubling Time \n


 


> - __Scopus__
> - 52,000,000 Registers \n
> - 4.13% Growth Rate  \n
> - 17 Years Doubling Time \n

>

### Segmented Growth



```{r, out.width='75%'}
# graphics
import('data/shelf_life_growth.txt') %>>% 
    as_tibble %>>% 
    rename(PY = V1, publications = V2 ) %>>% 
    dplyr::filter(PY %in% c(1980:2019)) %>>% 
    dplyr::arrange(PY) %>>% 
    dplyr::mutate(trend=1:n()) %>>% 
    (. -> d)

d$lnp <- log(d$publications)

PY <- d$PY

d$est <- ifelse(PY <= 1986.0, -441.3+(0.2239)*PY,
                ifelse(PY<=1992.0, -441.3 + (0.2239)*1986.0 + 0.0511*(PY-1986.0), 
                       ifelse(PY<=2004.8, -441.3 + (0.2239)*1986.0 + 0.0511*(1992.0-1986.0) + 0.1510*(PY-1992.0), 
                              -441.3 +  (0.2239)*1986.0 + 0.0511*(1992.0-1986.0) + 0.1510*(2004.8-1992.0) + 0.1186*(PY-2004.8)
                    )))

d %>>% 
    mutate(ln_Publications=lnp, Year=PY) %>>% 
    mutate(ln_Publications=round(ln_Publications,2), est=round(est,2)) %>>% 
    (. -> d2)

hchart(d2, "line", hcaes(x = Year, y = ln_Publications), name = "Publications", showInLegend = TRUE, fillOpacity = 0.2) %>>% 
    hc_add_series(d2, "line", hcaes(x = Year, y = est), name = "Segmented Regression", showInLegend = TRUE, fillOpacity = 0.2) %>>% 
    hc_add_theme(hc_theme_elementary()) %>>% 
    hc_navigator( enabled = TRUE) %>>% 
    hc_xAxis( plotBands = list( list( from = 1986, to = 1986, color = "#330000" ),
                                list( from = 1992, to = 1992, color = "#330000" ),
                                list( from = 2004, to = 2004, color = "#330000" )
                               ))
```


### Networks

```{r}
netcoup <- import('data/netcoup.rds') 
hubs <- import('data/netcoup_hubs.rds') 

hubs %>>% 
    select(SR,Ki) %>>% 
    (. -> hubs2)

netcoup %>>% 
    activate(nodes) %>>% 
    left_join(hubs2) %>>% 
    (. -> netcoup)

# ALTERAR AQUI
ano <- 1990 

netcoup %>>% 
    as_tbl_graph() %>>% 
    activate(nodes) %>>% 
    mutate(label=name) %>>% 
    mutate(label=paste( gsub(' .*$','',label), gsub('.*\\.','',label), sep='' )) %>>% 
    dplyr::filter(!is.na(grupo)) %>>% 
    dplyr::filter(PY <= ano) %>>%  
    (. -> netcoup2)

tibble(id=1:length(V(netcoup2)),
       label= V(netcoup2)$label,
       group=V(netcoup2)$grupo,
       year=V(netcoup2)$PY
       ) %>>% 
(. -> nodes)

tibble(from = netcoup2 %>>% activate(edges) %>>% as_tibble %>>%  pull(from),
       to = netcoup2 %>>% activate(edges) %>>% as_tibble %>>%  pull(to)
       ) %>>% 
(. -> edges)

visNetwork(nodes, edges, height = "700px", width = "100%", main = as.character(max(V(netcoup2)$PY))) %>%
    visNodes(size = 10, shape='dot') %>>% 
    visEdges(width = 2, hidden=F)  %>>% 
    visOptions(selectedBy = "group", highlightNearest = TRUE, nodesIdSelection = F) %>>%  
    visPhysics(stabilization = T) %>>% 
    visGroups(groupname = "g01", color = "#38501e") %>>% 
    visGroups(groupname = "g02", color = "#23331e") %>>% 
    visGroups(groupname = "g03", color = "#6e1d21") %>>% 
    visGroups(groupname = "g04", color = "#472926") %>>% 
    visGroups(groupname = "g05", color = "#926433") %>>% 
    visGroups(groupname = "g06", color = "#a90a26") %>>% 
    visGroups(groupname = "g07", color = "#97863e") %>>% 
    visGroups(groupname = "g08", color = "#00FFFF") %>>% 
    visGroups(groupname = "g09", color = "#d48d01") %>>% 
    visGroups(groupname = "g10", color = "#021338") %>>% 
    visGroups(groupname = "g11", color = "#e6d82e") %>>% 
    visGroups(groupname = "g12", color = "#9eb739") %>>% 
    visGroups(groupname = "g13", color = "#808080") 
```


### Groups Attributes
    
```{r}

grupos <- sort(unique(grupoAno$Group))

# grupos <- grupos[1:3]

res <- vector('double', length(grupos))

for(i in seq_along(grupos)){ 

    grupoAno %>>% 
        dplyr::select(PY,n,Group) %>>% 
        dplyr::rename(publications = n) %>>% 
        dplyr::filter(PY >= 2000) %>>% 
        dplyr::arrange(PY) %>>%  
        dplyr::filter(Group==grupos[[i]]) %>>%
        dplyr::mutate(trend=1:n()) %>>%
        dplyr::mutate(lnp=log(publications)) %>>%
        (. -> d) 

    # ajustar parametros via mqo 
    m1 <- lm(lnp ~ trend, data=d)
    beta0 <- m1$coefficients[[1]]
    beta1 <- m1$coefficients[[2]]
    
    # modelo não linear
    m2 <- nls(publications ~ b0*exp(b1*(PY-2010)), start = list(b0=beta0, b1=beta1), data=d)
    res[[i]] <- coef(m2)[2]

}

# print(xtable(grupoAnoCrescimento, type = "latex"))

data.frame(Groups=grupos,Coef=res) %>>% 
    as_tibble %>>% 
    mutate(GrowthRateYear=(exp(Coef)-1)*100) %>>% 
    dplyr::select(-Coef) %>>% 
    left_join(import('data/netcoup_grupos.rds') %>>% select(nname,qtde.papers,PY.m) %>>% rename(Groups = nname)) %>>% 
    dplyr::arrange(Groups) %>>% 
    (. -> grupoAnoCrescimento) %>>%     
    dplyr::rename(AverageAge = PY.m) %>>% 
    dplyr::rename(TotalPapers = qtde.papers) %>>% 
    mutate(AverageAge = round(AverageAge,1)) %>>% 
    left_join(import('data/ZiPi.rds') %>>% mutate(Groups=grupo) %>>% select(Groups,Hubs)) %>>% 
    mutate(Description='Adicionar a descrição do grupo. Manter um texto o mais explicativo possível.') %>>% 
    relocate(Description, .after=Groups) %>>% 
    select(-Description) %>>% 
    rename(Group = Groups) %>>% 
    datatable(options=list(pageLength=13, dom = 'tip'), rownames=F)  %>>% 
    formatRound('GrowthRateYear',1)
```

# g01 {data-navmenu="Groups"}

Em construção. 


# g02 {data-navmenu="Groups"}

Em construção. 


# g03 {data-navmenu="Groups"}

Em construção. 


# g04 {data-navmenu="Groups"}

Em construção. 


# Conclusions

Escrever algum texto para finalizar a análise.


# Pessoas  {.hidden}

Em construção. 


# Patentes  {.hidden}

Em construção.